M Nosrati Maralloo - Department of Computer Engineering , Science and Research Branch, Islamic Azad University, Tehran,Iran
A.R Koushki - Department of Computer Engineering , Science and Research Branch, Islamic Azad University, Tehran,Iran
C Lucas - Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, University of Tehran,Tehran,Iran
A Kalhor - Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, University of Tehran,Tehran,Iran

Long-term forecasting of load demand is necessary for the correct operation of electric utilities. There is an on-going attention toward putting new approaches to the task. Recently, Neurofuzzy modeling has played a successful role in various applications over nonlinear time series prediction. This paper presents a neurofuzzy model for long-term load forecasting. This model is identified through Locally Linear Model Tree (LoLiMoT) learning algorithm. The model is compared to a multilayer perceptron and hierarchical hybrid neural model (HHNM). The models are trained and assessed on load data extracted from a North- American electric utility.